CN108259397B - Large-scale MIMO system channel estimation method based on adaptive regularization subspace tracking compressed sensing algorithm - Google Patents
Large-scale MIMO system channel estimation method based on adaptive regularization subspace tracking compressed sensing algorithm Download PDFInfo
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Abstract
The invention discloses a large-scale MIMO system channel estimation based on a self-adaptive regularization subspace tracking compressed sensing algorithm, which aims at large scaleThe sparsity of the MIMO channel in the time domain, and the design of a channel estimation algorithm based on compressed sensing, have the following steps: at base station NtRoot antenna sending information, N of user siderReceiving by a root antenna, wherein a received pilot signal is a measurement vector y; constructing a perception matrix A according to the transmitted pilot frequency information; the adaptive regularization subspace tracking algorithm estimates the sparse signal h. The method adopted by the invention is researched on the premise of channel sparsity, is improved on the basis of a subspace tracking algorithm, is self-adaptively selected during the first step length selection, adds a regularization process between two times of atom selection, selects a group of atoms with the largest energy, can obtain a more accurate estimation effect with less pilot frequency number, has better effect than the traditional channel estimation method, and has certain practical value.
Description
Technical Field
The invention belongs to the field of communication signal processing, and particularly relates to a large-scale MIMO system channel estimation method based on a self-adaptive regularization subspace tracking compressed sensing algorithm.
Background
In recent years, mobile communication wireless technology has been rapidly developed, and in the fourth generation mobile communication, MIMO technology has greatly improved the rate and reliability of information transmission by using spatial multiplexing and transmission diversity characteristics. In the next decade, the transmission rate requirement of wireless communication is expected to be thousands times that of the current system, so that 4G still has difficulty in meeting the application requirement of future mobile communication, and many countries have focused on the research of fifth generation mobile communication technology. The core idea of the large-scale MIMO technology is to equip tens or even hundreds of antennas at the base station end to form an antenna array to simultaneously serve multiple users to improve the spectrum utilization and improve the information transmission rate, which has become one of the 5G key technologies. Accurate channel state information is needed for channel equalization and related detection of massive MIMO technology, so it is necessary to perform channel estimation for massive MIMO systems.
At present, in the field of large-scale MIMO system channel estimation, research on large-scale MIMO channel estimation is mostly directed at a TDD transmission mode at the present stage, and because TDD has reciprocity of uplink and downlink channels, transposing the channel matrix estimated by using the uplink channel is the downlink channel state information, thereby avoiding the problem of pilot pollution caused by too many antennas at a base station in a large-scale MIMO system. However, this channel reciprocity is not real-time and the information of the uplink channel may be outdated and inaccurate for the downlink channel. And FDD is still the mainstream of the cellular system in the present cell, so it is necessary to study the downlink channel estimation in the FDD transmission mode. Due to the limited amount of scattering and delay spread in the signal propagation space and the spatial correlation of the antennas at the base station, the energy of the channel is concentrated on only a few main paths, and the energy on other paths is small and negligible, so that the channel is regarded as sparse in the time domain. In the research of FDD downlink channel estimation of a large-scale MIMO system, conventional channel estimation methods, such as Minimum Mean Square Error (MMSE) algorithm and least square method (LS), do not utilize the sparseness of a channel, require more pilot signals, waste frequency band resources and have poor noise immunity.
Disclosure of Invention
Aiming at the defects of the existing channel estimation method, the invention provides a large-scale MIMO system channel estimation method based on a self-adaptive regularization subspace tracking compressed sensing algorithm. In the actual transmission space, because there are a limited amount of scattering and delay spread, and the antennas at the base station are arranged compactly and have spatial correlation, the invention is carried out on the premise of sparse channel time domain, and the technical steps are as follows:
a large-scale MIMO system channel estimation method based on an adaptive regularization subspace tracking compressed sensing algorithm is disclosed, wherein the channel is regarded as sparse in the time domain, and the method comprises the following steps:
s1, base station NtRoot antenna sending information, N of user siderReceiving by root antennaThe received pilot signal is a measurement vector y; constructing a perception matrix A according to the transmitted pilot frequency information;
and S2, estimating the sparse signal h by an adaptive regularized subspace tracking algorithm.
The specific steps of step S1 are as follows:
an OFDM symbol with U sub-carriers is sent by the ith antenna of a sending end, IFFT conversion is carried out to realize OFDM modulation, a cyclic prefix CP is added in front of each output OFDM symbol to weaken the influence generated by channel delay expansion, the processed OFDM signals are transmitted to the antenna of each user end in a wireless channel after digital-to-analog conversion, the cyclic prefix CP is removed and FFT conversion is carried out on the jth receiving antenna, and a pilot signal received by a receiving end is a measurement vector yj，y＝yj；
Randomly selecting N position pilot frequency symbols on U sub-carriers to construct a sensing matrix
Wherein p isnFor pilot information of selected N positions, FLN rows of the position of the corresponding pilot frequency in the U-point discrete Fourier transform matrix F and the first L columns of the channel length
The channel impulse response thus obtained can be solved using a compressed perceptual model.
And then reconstructing a channel impulse response by using a compressed sensing reconstruction algorithm, wherein the reconstruction algorithm uses an improved greedy algorithm, namely an adaptive regularization subspace tracking algorithm:
the specific steps of step S2 are as follows:
s21, reconstruction initialization: initial residual error: r is0Y, number of iterations: i is 1, initial step size: s is 1, step length: stage 1, column index set:
perceptual matrix support set:
sparsity K;
s22, according to the formula u ═ uj|uj＝|<rj,Aj>I, J is 1,2, …, N, calculating the inner product of residual r and each row of sensing matrix A, and selecting the first s maximum values to be recorded in index set Ji；
S23, pair JiMiddle pressing type | ui|≤2|uj|,i,j∈JiPerforming regularization energy grading process, and storing the index value obtained after regularization into JxAnd according to JxUpdating support set AJx；
S24, obtaining
Pseudo-inverse of
S25, selecting according to the backtracking thought
The index value corresponding to the K elements with the maximum absolute value is recorded in JtAnd according to the index value JtUpdating support set
S26, obtaining
Pseudo-inverse of
S27, residual error updating
S28, if i is equal to or less than K, continuing, otherwise, stopping iteration and entering step S210;
s29, comparing residual values:
if rnew||2≥||rn-1||2If yes, then step S22 is executed;
if rnew||2<||rn-1||2If S is S, go to step S22;
s210, reconstruction
At JtHas a non-zero value which is obtained in the last iteration
And S211, performing the same processing on the information received by other receiving antennas, and taking a union set to obtain the final estimated h.
The traditional channel estimation technology does not consider the channel sparsity characteristic, and can obtain a certain estimation effect by using an excessive number of pilot frequencies, thereby greatly reducing the frequency band utilization rate and wasting resources. The method adopted by the invention is researched on the premise of channel sparsity, is improved on the basis of a subspace tracking algorithm, is self-adaptively selected during the first step length selection, adds a regularization process between two times of atom selection, selects a group of atoms with the largest energy, can obtain a more accurate estimation effect with less pilot frequency number, has better effect than the traditional channel estimation method, and has certain practical value.
For the above reasons, the present invention can be widely applied to the fields of communication signal processing and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a large-scale MIMO system channel estimation method based on an adaptive regularization subspace tracking compressed sensing algorithm in an embodiment of the present invention.
Fig. 2 is a transmission flow of a massive MIMO system in an embodiment of the present invention.
FIG. 3 is a flow chart of adaptive regularization subspace tracking algorithm estimation in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-fig. 3, a large-scale MIMO system channel estimation method based on an adaptive regularization subspace tracking compressed sensing algorithm is used for a downlink channel of a single cell FDD transmission mode. Configuring N at a base stationtRoot antenna of NrA single antenna user terminal. In this embodiment, N is usedt＝16，NrThe specific description is made as 8.
The channel is considered sparse in the time domain, and the estimation has the following steps:
s1, base station NtRoot antenna sending information, N of user siderReceiving by the root antenna, wherein the received pilot signal is a measurement vector y: the channel impulse response between the ith transmitting antenna and the jth receiving antenna is
hiFor path gain, τiFor path delay, the channel length is L, hiThe number of the non-zero medium is K, and K is less than L. Taking the length of a channel as 256, and taking the number K of nonzero channels as 6, namely the sparsity of the channel as 6;
sending an OFDM symbol with 4096 subcarriers at the ith antenna of a sending end, carrying out IFFT (inverse fast Fourier transform) to realize OFDM modulation, adding a Cyclic Prefix (CP) in front of each output OFDM symbol, carrying out digital-to-analog conversion on the processed OFDM signals, transmitting the processed OFDM signals to the antenna of each user end in a wireless channel, removing the CP and FFT (fast Fourier transform) at the jth receiving antenna, and taking the pilot signals received by a receiving end as measurement vectors yj，y＝yjConsidering the noise n in the channel, the received symbol received by the jth user is of the formula
S2, constructing a perception matrix A according to the sent pilot frequency information:
randomly selecting 500 position pilot frequency symbols on 4096 sub-carriers to construct sensing matrix
Wherein p isnFor pilot information of 500 selected positions, FLIs 500 rows of the position of the corresponding pilot frequency in the U-point discrete Fourier transform matrix F and the first 256 columns of the channel length, then
S3, estimating a sparse signal h by using an adaptive regularization subspace tracking algorithm:
in a large-scale MIMO system, due to the fact that a limited amount of scattering and delay spread exist in a signal propagation space and spatial correlation exists between antennas at a base station, energy of a channel is concentrated on a few main paths, energy on other paths is small and can be ignored, the channel is regarded as sparse in a time domain, and estimation is carried out by adopting a compressed sensing method according to the characteristic of channel sparsity. The compressed sensing is newSampling theory, which is different from the conventional nyquist sampling theorem. It is pointed out that, as long as the signal is compressible or sparse over some transform domain, one can project the high-dimensional signal onto a low-dimensional space with an observation matrix that is not related to the transform basis, and then reconstruct the original signal with high probability from these small number of projections by solving an optimization problem. Suppose in RNThe space has a signal x of N × 1 dimension, x can be composed of a signal x in RNA sparse representation of a transform basis Ψ matrix of N × N in space, i.e., x ═ Ψ h;
h is a compressible sparse signal, i.e. only K values in h are non-zero. The compressed sensing theory shows that for a sparse signal h in a certain Ψ domain, h can be projected onto y by using an M × N-dimensional measurement matrix Φ irrelevant to the transform domain Ψ, so as to obtain a compressed sensing measurement model, as shown in the formula y ═ Φ x ═ Φ Ψ h ═ Ah.
Wherein y is a measurement vector of dimension 500 × 1 of N × 1, and A is N × (N)tL) dimension, i.e. 500 × 4096 the sparse signal h can be reconstructed using the known observation vector y and the perception matrix a.
In order to solve the compressed sensing reconstruction problem, the sensing matrix a needs to satisfy a Restricted Isometric Property (RIP), but it is difficult to verify whether the sensing matrix satisfies the RIP, and usually, the sensing matrix a can be solved as long as the transformation matrix Ψ is not related to the measurement matrix Φ.
S31, input N × (N)tL) dimension of the sensing matrix a, N × 1 dimension of the observation vector y, step s being 1, sparsity K;
carrying out reconstruction initialization: initial residual error: r is0Y, number of iterations: i is 1, initial step size: s is 1, step length: stage 1, column index set:
perceptual matrix support set:
sparsity K;
s32, according to the formula u ═ uj|uj＝|<rj,Aj>I, J is 1,2, …, N, calculating the inner product of residual r and each row of sensing matrix A, and selecting the first s maximum values to be recorded in index set Ji；
S33, pair JiMiddle pressing type | ui|≤2|uj|,i,j∈JiPerforming regularization energy grading process, and storing the index value obtained after regularization into JxAnd according to JxUpdating support set AJx；
S34, obtaining
Pseudo-inverse of
S35, selecting according to the backtracking thought
The index value corresponding to the K elements with the maximum absolute value is recorded in JtAnd according to the index value JtUpdating support set
S36, obtaining
Pseudo-inverse of
S37, residual error updating
S38, if i is equal to or less than K, continuing, otherwise, stopping iteration and entering step S310;
s39, comparing residual values:
if rnew||2≥||rn-1||2If yes, then step S32 is executed;
if rnew||2<||rn-1||2If S is S, go to step S32;
s310, reconstruction
At JtHas a non-zero value which is obtained in the last iteration
S311, the information received by other receiving antennas is processed in the same way, and the union set is taken to obtain the final estimated h.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (1)
1. A large-scale MIMO system channel estimation method based on an adaptive regularization subspace tracking compressed sensing algorithm is disclosed, wherein the channel is regarded as sparse in the time domain, and the method is characterized by comprising the following steps:
s1, base station NtRoot antenna sending information, N of user siderReceiving by a root antenna, wherein a received pilot signal is a measurement vector y; constructing a perception matrix A according to the transmitted pilot frequency information;
s2, estimating a sparse signal h by using a self-adaptive regularization subspace tracking algorithm;
the specific steps of step S1 are as follows:
sending an OFDM symbol with U subcarriers at the ith antenna of a sending end, carrying out IFFT (inverse fast Fourier transform) to realize OFDM modulation, adding a Cyclic Prefix (CP) in front of each output OFDM symbol, carrying out digital-to-analog conversion on the processed OFDM signals, transmitting the processed OFDM signals to the antenna of each user end in a wireless channel, removing the CP and FFT (fast Fourier transform) at the jth receiving antenna, and taking a pilot signal received by a receiving end as a measurement vector yj，y＝yj；
Randomly selecting N position pilot frequency symbols on U sub-carriers to construct a sensing matrix A ═
Wherein p isnFor pilot information of selected N positions, FLN rows of the position of the corresponding pilot frequency in the U-point discrete Fourier transform matrix F and the first L columns of the channel length
Wherein
For the transmitted pilot frequency time domain signal, H (i, j) is a channel frequency domain response matrix, H (i, j) is a channel time domain sparse signal, and n is channel noise;
the specific steps of step S2 are as follows:
s21, reconstruction initialization: initial residual error: r is0Y, number of iterations: i is 1, initial step size: s is 1, step length: stage 1, column index set:
perceptual matrix support set:
sparsity K;
s22, according to the formula u ═ uj|uj＝|<rj,Aj>I, J is 1,2, …, N, calculating the inner product of residual r and each row of sensing matrix A, and selecting the first s maximum values to be recorded in index set Ji；
S23, pair JiMiddle pressing type | ui|≤2|uj|,i,j∈JiPerforming regularization energy grading process, and storing the index value obtained after regularization into JxAnd according to JxUpdating support set AJx；
S24, obtaining
Pseudo-inverse of
S25, selecting according to the backtracking thought
The index value corresponding to the K elements with the maximum absolute value is recorded in JtAnd according to the index value JtUpdating support set
S26, obtaining
Pseudo-inverse of
S27, residual error updating
S28, if i is equal to or less than K, continuing, otherwise, stopping iteration and entering step S210;
s29, comparing residual values:
if rnew||2≥||rn-1||2If yes, then step S22 is executed;
if rnew||2＜||rn-1||2If S is S, go to step S22;
s210, reconstruction
At JtHas a non-zero value which is obtained in the last iteration
S211, performing the same processing on the information received by other receiving antennas, and taking a union set to obtain the final estimated h;
wherein r isnewFor the residual of this iteration, rn-1For the residual of the last iteration, | rnew||2Is 2 norm of the residual value of the iteration, i.e. the residual energy, | | rn-1||2The 2 norm of the residual value for the last iteration is the residual energy.
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| CN105162472A (en) * | 2015-08-24 | 2015-12-16 | 电子科技大学 | Block sparse signal reconstruction method based on greedy iteration |
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